Abstract

The novel coronavirus 2019 (COVID-2019), which first appeared in Wuhan city of China in December 2019, spread rapidly around the world and became a pandemic. It has caused a devastating effect on both daily lives, public health, and the global economy. It is critical to detect the positive cases as early as possible so as to prevent the further spread of this epidemic and to quickly treat affected patients. The need for auxiliary diagnostic tools has increased as there are no accurate automated toolkits available. Recent findings obtained using radiology imaging techniques suggest that such images contain salient information about the COVID-19 virus. Application of advanced artificial intelligence (AI) techniques coupled with radiological imaging can be helpful for the accurate detection of this disease, and can also be assistive to overcome the problem of a lack of specialized physicians in remote villages. In this study, a new model for automatic COVID-19 detection using raw chest X-ray images is presented. The proposed model is developed to provide accurate diagnostics for binary classification (COVID vs. No-Findings) and multi-class classification (COVID vs. No-Findings vs. Pneumonia). Our model produced a classification accuracy of 98.08% for binary classes and 87.02% for multi-class cases. The DarkNet model was used in our study as a classifier for the you only look once (YOLO) real time object detection system. We implemented 17 convolutional layers and introduced different filtering on each layer. Our model (available at (https://github.com/muhammedtalo/COVID-19)) can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients.

Keywords

Computer scienceCoronavirus disease 2019 (COVID-19)Artificial intelligenceConvolutional neural networkBinary classificationClassifier (UML)Artificial neural networkPattern recognition (psychology)Local binary patternsMachine learningComputer visionMedicineSupport vector machinePathologyInfectious disease (medical specialty)DiseaseImage (mathematics)

MeSH Terms

BetacoronavirusCOVID-19Computational BiologyCoronavirus InfectionsDatabasesFactualDeep LearningDiagnosisComputer-AssistedFemaleHumansLungMaleMiddle AgedNeural NetworksComputerPandemicsPneumoniaPneumoniaViralRadiographic Image InterpretationComputer-AssistedSARS-CoV-2

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Publication Info

Year
2020
Type
article
Volume
121
Pages
103792-103792
Citations
2619
Access
Closed

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Cite This

Tülin Öztürk, Muhammed Talo, Eylul Azra Yildirim et al. (2020). Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in Biology and Medicine , 121 , 103792-103792. https://doi.org/10.1016/j.compbiomed.2020.103792

Identifiers

DOI
10.1016/j.compbiomed.2020.103792
PMID
32568675
PMCID
PMC7187882

Data Quality

Data completeness: 86%